A method for simulating familial disease data with variable age at onset and genetic and environmental effects

The field of genetic epidemiology is growing rapidly with the realization that many important diseases are influenced by both genetic and environmental factors. For this reason, pedigree data are becoming increasingly valuable as a means of studying patterns of disease occurrence. Analysis of pedigree data is complicated by the lack of independence among family members and by the non-random sampling schemes used to ascertain families. An additional complicating factor is the variability in age at disease onset from one person to another. In developing statistical methods for analysing pedigree data, analytic results are often intractable, making simulation studies imperative for assessing the performance of proposed methods and estimators. In this paper, an algorithm is presented for simulating disease data in pedigrees, incorporating variable age at onset and genetic and environmental effects. Computational formulas are developed in the context of a proportional hazards model and assuming single ascertainment of families, but the methods can be easily generalized to alternative models. The algorithm is computationally efficient, making multi-dataset simulation studies feasible. Numerical examples are provided to demonstrate the methods.     

A genetic algorithm for thepart family formation problem

Recently, there has been a lot of interest in group technology (GT) from researchers as well as from practitioners. This interest is explained by the fact that GT supports new manufacturing philosophies. One of the main issues in GT is the part family formation problem which is concerned with grouping similar products into same families. Many researchers have tackled this problem and many algorithms have been proposed for it. In this paper, we present a genetic technique-based heuristic for the quadratic integer programming model of the part family formation problem which was formulated by Kusiak et al. (1986). The heuristic is tested on several problems from the literature, and preliminary results are very promising.     

Comparative evaluation of genetic algorithms for job-shop scheduling

Many optimization problems from the industrial engineering world, in particular the manufacturing systems, are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest in imitating living beings to solve such kinds of hard optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization tech niques called evolutionary algorithms, which can often outperform conventional optimization methods when applied to difficult real-world problems. There are currently three main avenues of this research: genetic algorithms (GAs), evolutionary programming (EP) and evolution strategies (ESs). Among them, genetic algorithms are perhaps the most widely known types of evolutionary algorithms today. During the past years, several GAs for the job-shop scheduling problems have been proposed, each with different chromosome representation. In this paper, the different GAs are collected from the literature and an attempt has been made to evaluate them. The benchmark problems available in open literature are used for evaluation and the performance measure considered is makespan. The algorithms are coded in C+ +.     

面向Agent马尔科夫多议题自动谈判模型研究

为增强自动谈判模型中各谈判Agent在报价与反报价过程中的科学性和合理性,提高谈判的效率和成功的概率,将马尔科夫链方法应用于对谈判对方各轮报价的预测当中,以增强模型对谈判过程的逻辑推理能力,通过与改进后的遗传算法相结合,构建了面向Agent的马尔科夫多议题自动谈判模型,期望为谈判各方在谈判过程中的报价提供有力支持。通过仿真计算可以看出,所提模型很好地对谈判过程进行了仿真和推理,多种方法的有效结合,能够客观地为谈判各方Agent提供合理决策的有效依据。     

结构主义、自然科学与经济学的变革

经济学与自然科学的元素结构具有惊人的相似性。运用结构主义方法,可以改进经济学的基因结构。与生物DNA的基本片段相比较,《资本论》的基因片段尚未健全。由于历史限制,马克思尚未建立起多维对称的基因结构。但《资本论》的基因片段可以自我复制、自我完善。《资本论》基因片段的自我完善,对于建立经济学元素周期率和经济学DNA结构,对于21世纪经济学的变革意义重大。     

不确定条件下新产品供应链零部件供应商选择的优化研究

本文运用可信性理论建立了模糊需求条件下新产品供应链配置的模糊机会约束规划模型，对模型的求解进行了分析，并用遗传算法进行求解。算例分析表明，运用本文提出的方法可以从供应商的选择、阶段库存和客户服务水平三个方面对模糊需求条件下的新产品供应链进行配置优化。     

遗传算法在WMS货位优化分配中的应用

从一汽轿车股份有限公司仓储中心WMS的实际业务出发,考虑物料的进出库频率和货架的受力情况,为货位分配问题建立多目标优化模型.利用遗传算法中的并列选择法,设计遗传算子并进行求解.优化后结果表明:可以减少堆垛机的运行距离,合理安排货物的分配,减少人工作业,提高仓储的利用效率,降低企业成本.     

Genetic Algorithm in the Wavelet Domain for Large p Small n Regression

Many areas of statistical modeling are plagued by the “curse of dimensionality,” in which there are more variables than observations. This is especially true when developing functional regression models where the independent dataset is some type of spectral decomposition, such as data from near-infrared spectroscopy. While we could develop a very complex model by simply taking enough samples (such that n > p), this could prove impossible or prohibitively expensive. In addition, a regression model developed like this could turn out to be highly inefficient, as spectral data usually exhibit high multicollinearity. In this article, we propose a two-part algorithm for selecting an effective and efficient functional regression model. Our algorithm begins by evaluating a subset of discrete wavelet transformations, allowing for variation in both wavelet and filter number. Next, we perform an intermediate processing step to remove variables with low correlation to the response data. Finally, we use the genetic algorithm to perform a stochastic search through the subset regression model space, driven by an information-theoretic objective function. We allow our algorithm to develop the regression model for each response variable independently, so as to optimally model each variable. We demonstrate our method on the familiar biscuit dough dataset, which has been used in a similar context by several researchers. Our results demonstrate both the flexibility and the power of our algorithm. For each response variable, a different subset model is selected, and different wavelet transformations are used. The models developed by our algorithm show an improvement, as measured by lower mean error, over results in the published literature.     

DNA指纹与性状间的相关度量

根据资料总体分布特征,推导出ＤＮＡ 指纹与两种类型性状间的相关度量公式。论述了使用该公式筛选标记性状指纹的合理性,同时给出了相应的计算和统计分析方法     

Two-sided assembly line balancing: A genetic algorithm approach

A two-sided assembly line balancing problem is typically found in plants producing large-sized high-volume products, e.g. buses and trucks. The features specific to the assembly line are described in this paper, which are associated with those of: (i) two-sided assembly lines; (ii) positional constraints; and (iii) balancing at the operational time. There exists a large amount of literature in the area of line balancing, whereby it has mostly dealt with one-sided assembly lines. A new genetic algorithm is developed to solve the problem, and its applicability and extensibility are discussed. A genetic encoding and decoding scheme, and genetic operators suitable for the problem are devised. This is particularly emphasized using problem-specific information to enhance the performance of the genetic algorithm (GA). The proposed GA has a strength that it is flexible in solving various types of assembly line balancing problems. An experiment is carried out to verify the performance of the GA, and the results are reported.